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Cropformer: An interpretable deep learning framework for crop genomic prediction

文献类型： 外文期刊

作者： Wang, Hao 1 ; Yan, Shen 1 ; Wang, Wenxi 2 ; Chen, Yongming 2 ; Hong, Jingpeng 4 ; He, Qiang 1 ; Diao, Xianmin 1 ; Lin, Yunan 5 ; Chen, Yanqing 1 ; Cao, Yongsheng 1 ; Guo, Weilong 2 ; Fang, Wei 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China

2.China Agr Univ, Frontiers Sci Ctr Mol Design Breeding, Key Lab Crop Heterosis & Utilizat MOE, Beijing 100193, Peoples R China

3.China Agr Univ, Beijing Key Lab Crop Genet Improvement, Beijing 100193, Peoples R China

4.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

5.Tech Univ Munich, Sch Engn & Design, D-85521 Munich, Germany

6.Peking Univ, Inst Adv Agr Sci, State Key Lab Wheat Improvement, Shandong Lab Adv Agr Sci, Weifang 261325, Shandong, Peoples R China

关键词： deep learning; genomic selection; multiple self-attention mechanisms; phenotypic prediction

期刊名称：PLANT COMMUNICATIONS （ 影响因子：11.6； 五年影响因子：11.8 ）

ISSN： 2590-3462

年卷期： 2025 年 6 卷 3 期

页码：

收录情况： SCI

摘要： Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability. To address these challenges, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy. The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness, achieving up to a 7.5% improvement in prediction accuracy compared to the runner-up model. Additionally, Cropformer enhances the analysis and mining of genes associated with traits. We identified numerous single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences. Cropformer represents a significant advancement in predictive performance and gene identification, providing a powerful general tool for improving genomic design in crop breeding. Cropformer is freely accessible at https://cgris.net/ cropformer.

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